Methods and systems for adapting a model for a speech recognition system

ABSTRACT

Methods are disclosed for identifying possible errors made by a speech recognition system without using a transcript of words input to the system. A method for model adaptation for a speech recognition system includes determining an error rate, corresponding to either recognition of instances of a word or recognition of instances of various words, without using a transcript of words input to the system. The method may further include adjusting an adaptation, of the model for the word or various models for the various words, based on the error rate. Apparatus are disclosed for identifying possible errors made by a speech recognition system without using a transcript of words input to the system. An apparatus for model adaptation for a speech recognition system includes a processor adapted to estimate an error rate, corresponding to either recognition of instances of a word or recognition of instances of various words, without using a transcript of words input to the system. The apparatus may further include a controller adapted to adjust an adaptation of the model for the word or various models for the various words, based on the error rate.

RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 11/331,649, entitled “Methods and Systems for ConsideringInformation About an Expected Response When Performing SpeechRecognition” filed Jan. 13, 2006, which is a continuation-in-partapplication of U.S. patent application Ser. No. 11/051,825, entitled“Method and System for Considering Information About an ExpectedResponse When Performing Speech Recognition” filed Feb. 4, 2005, whichall applications are incorporated herein by reference in entirety. Thisapplication also claims the benefit of U.S. Provisional Application No.60/788,621, entitled “Methods and Systems for Adapting a Model for aSpeech Recognition System”, filed Apr. 3, 2006, U.S. ProvisionalApplication No. 60/788,606, entitled “Methods and Systems for OptimizingModel Adaptation for a Speech Recognition System”, filed Apr. 3, 2006,and U.S. Provisional Application No. 60/788,622, entitled “Method andSystems for Assessing and Improving the Performance of a SpeechRecognition System”, filed Apr. 3, 2006, which all applications areincorporated herein by reference in entirety.

TECHNICAL FIELD

The invention relates to speech recognition and, more particularly, toimproving the accuracy and efficiency of speech recognition systems.

BACKGROUND

Speech recognition systems have simplified many tasks particularly for auser in the workplace by permitting the user to perform hands-freecommunication with a computer as a convenient alternative tocommunication via conventional peripheral input/output devices. Forexample, a user could wear a wireless wearable terminal having a speechrecognition system that permits communication between the user and acentral computer system so that the user can receive work assignmentsand instructions from the central computer system. The user could alsocommunicate to the central computer system information such as dataentries, questions, work progress reports, and work condition reports.In a warehouse or inventory environment, a user can be directed (throughan audio instruction from the central computer system or visually bymeans of a display) to a particular work area that is labeled with amultiple-digit number (check-digit) such as “1-2-3” and be asked tospeak the check-digit. The user would then respond with the expectedresponse “1-2-3”. (Note that a “check-digit” can be any word or sequenceof words, and is not limited to digits.) Other such examples ofapplications and communications where knowledge about the response isknown are described in U.S. Patent Application No. 2003/0154075 andinclude environments where a wearable or portable terminal is notrequired such as in an automobile or a telephone system; environmentsthat are not in a warehouse such as in a pharmacy, retail store, andoffice; voice-controlled information processing systems that process forexample credit card numbers, bank account numbers, social securitynumbers and personal identification numbers; other applications such ascommand and control, dictation, data entry and information retrievalapplications; and speech recognition system features such as userverification, password verification, quantity verification, andrepeat/acknowledge messages. The inventions presented here can be usedin those applications. In using a speech recognition system, manual dataentry is eliminated or at the least reduced, and users can perform theirtasks faster, more accurately, and more productively.

Errors can be made by a speech recognition system however, due to forexample background noise or a user's unfamiliarity or misuse of thesystem. The errors made by a system can be classified into varioustypes. A metric, the word error rate (which can be defined as thepercentage or ratio of speech recognition errors over the number ofwords input to the system and which can be determined over a window oftime and/or data and per user) is often used to evaluate the number andtypes of errors made by a speech recognition system and is thus usefulin evaluating the performance of the system. In general, a word errorrate can be determined for a word or for various words among a set ofwords, or for a user or multiple users. Identification of a system'serrors can be done by comparing a reference transcription of a user'sinput speech to the hypothesis generated by the system (the system'sinterpretation of the user's input speech). Furthermore, as known tothose skilled in the art, the comparison can be performed in atime-aligned mode or in a text-aligned mode.

One type of speech recognition error is a substitution, in which thespeech recognition system's hypothesis replaces a word that is in thereference transcription with an incorrect word. For example, if systemrecognizes “1-5-3” in response to the user's input speech “1-2-3”, thesystem made one substitution: substituting the ‘5’ for the ‘2’.

Another type of speech recognition error is a deletion, in which thespeech recognition system's hypothesis lacks a word that is in thereference transcription. For example, if system recognizes “1-3” inresponse to the user's input speech “1-2-3”, the system deleted oneword, the ‘2’. There are many types of deletion errors. One variation ofthe deletion error is a deletion due to recognizing garbage, in whichthe system erroneously recognizes a garbage model instead of recognizingan actual word. Another variation of the deletion error is a deletiondue to a speech misdetection, where the system fails to detect that theaudio input to the system contains speech and as a result does notsubmit features of the audio input to the system's search algorithm.Another type of deletion occurs when the system rejects a correctrecognition due to a low confidence score. Yet another variation of thedeletion error is a deletion due to a rejected substitution, where asearch algorithm of the speech recognition generates a substitutionwhich is later rejected by an acceptance algorithm of the system. Stillanother type of deletion, occurring in time-aligned comparisons, is amerge: the speech recognition system recognizes two spoken words as one.For example, the user says “four two” and the system outputs “forty”.

In this application, a garbage model refers to the general class ofmodels for sounds that do not convey information. Examples may includefor example models of breath noises, “um”, “uh”, sniffles, wind noise,the sound of a pallet dropping, the sound of a car door slamming, orother general model such as a wildcard. (A wildcard is intended to matchthe input audio for any audio that doesn't match a model in the libraryof models.)

Yet another type of speech recognition error is an insertion, in whichthe speech recognition system's hypothesis includes a word (or symbol)that does not correspond to any word in the reference transcription.Insertion errors often occur when the system generates two symbols thatcorrespond to one symbol. One of these symbols may correspond to thereference transcription and be tagged as a correct recognition. If itdoes not correspond to the reference transcription, it can be tagged asa substitution error. In either case, the other symbol can be tagged asan insertion error. Insertion errors are also common when noise ismistakenly recognized as speech.

In contrast to determining that an actual error occurred by comparing asystem's hypothesis to words actually spoken in a reference transcript,an error can be estimated or deemed to have occurred based on systembehavior and user behavior. Accordingly, one can estimate or evaluatethe performance level of the speech recognition system, by detecting inthis manner the various errors committed by the system. One way todetect a speech recognition error is based on feedback a user providesto the speech recognition system. Feedback can be requested by thespeech recognition system. For example, the system could ask the user toconfirm the system's hypothesis by asking the user for example “Did yousay 1-5-3?”, and if the user responds “no”, it indicates that the systemmade an error recognizing “1-5-3”. Another type of feedback is based ona user's emotion detected by speech recognition. For example, if thesystem recognizes in the user's input speech that the user is sighing orsaying words indicating aggravation, it may indicate that an erroroccurred. Yet another type of feedback is based on a user's correctioncommand to the system, such as the user speaking “back-up” or “erase”,or the user identifying what word was spoken (which could be from a listof possible words displayed by the system). When a correction iscommanded to the system, it may indicate that an error occurred.

A speech recognition system can improve its performance over time, asmore speech samples are received and processed by a speech recognitionsystem, by improving its acoustic models through training or otherlearning or adaptation algorithms. At the same time, it is useful toprevent the system from adapting in an undesirable way, therebyresulting in a system that performs worse than it did prior toadaptation or a system that degrades over time. Avoiding additionalprocessing by a speech recognition system due to adaptation of acousticmodels is particularly useful in many applications, particularly thoseemploying a battery powered mobile computer, wireless network, andserver to store models. Adapting models can use significantcomputational resources to create the adapted models and radiotransmission energy to transmit the new models to the server. Exampleembodiments of the invention disclosed herein can control the rate ofadaptation of the speech recognition system to avoid inefficient use ofcomputational, storage and/or power resources and to avoid adapting awayfrom well-performing models. Example embodiments of the inventioncontrol adaptation by using triggers, which are based on an error ratedetermination (which may be based on an error rate estimation), to causethe adaptation of prior models or create new models. The invention alsodiscloses methods by which recognition error rates can be estimated.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate speech recognition systemcomponents and embodiments of the invention and, together with thedetailed description of the embodiments given below, serve to explainthe principles of the invention.

FIG. 1A illustrates a perspective view of a user using a portableterminal and headset, according to an example embodiment of theinvention;

FIG. 1B illustrates a schematic view of a speech recognition system,according to an example embodiment of the invention;

FIG. 2 illustrates a schematic view of a component of a speechrecognition system, according to an example embodiment of the invention;

FIG. 3 is a flowchart illustrating a method for controlling modeladaptation based on an error rate determination or estimation, accordingto an example embodiment of the invention;

FIGS. 4-6 are flowcharts illustrating methods for estimating an errorrate, according to example embodiments of the invention; and

FIG. 7 is a flowchart illustrating a method for model adaptation,according to an example embodiment of the invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE INVENTION

Example embodiments of the invention disclosed herein control the rateof adaptation of the speech recognition system, using triggers to causeadaptation of prior models or create new models. As a result, exampleembodiments avoid inefficient use of system resources and avoid adaptingaway from well-performing models. Example embodiments of the inventioninclude various error rate determinations (which may be based on errorrate estimations) which can be used as triggers for model adaptation.Note that in this description, references to “one embodiment” or “anembodiment” mean that the feature being referred to is included in atleast one embodiment of the invention. Further, separate references to“one embodiment” in this description do not necessarily refer to thesame embodiment; however, neither are such embodiments mutuallyexclusive, unless so stated and except as will be readily apparent tothose skilled in the art. Thus, the invention can include any variety ofcombinations and/or integrations of the embodiments described herein.

In one approach, a method for efficient use of model adaptationresources of a speech recognition system includes determining an errorrate, corresponding to either recognition of instances of a word orrecognition of instances of various words among a set of words. Themethod may further include adjusting an adaptation of a model for theword or various models for the various words, based on the error rate.The approach may be implemented in an apparatus which may include all ora subset of the following: a processor adapted to determine an errorrate, corresponding to either recognition of instances of a word orrecognition of instances of various words among a set of words; and acontroller adapted to adjust an adaptation of a model for the word orvarious models for the various words, based on the error rate.

In another approach, a method for identifying a possible error made by aspeech recognition system includes identifying an instance of a wordthat was recognized by the system within a certain confidence factorrange. The approach may be implemented as an apparatus which includes aprocessor adapted to identify an instance of a word that was recognizedby the system within a certain confidence factor range.

Yet in another approach, a method for identifying a possible error madeby a speech recognition system includes identifying an instance wherethe-system rejects a first hypothesis of a first utterance, followed bythe system accepting a second hypothesis of a second utterance, whereinthe first and second hypotheses substantially match word-for-word. Theapproach may be implemented as an apparatus which includes a processoradapted to identify an instance where the system rejects a firsthypothesis of a first utterance, followed by the system accepting asecond hypothesis of a second utterance, wherein the first and secondhypotheses substantially match word-for-word.

In yet another approach, a method for identifying a possible error madeby a speech recognition system includes identifying when the systemgenerates a first hypothesis of a first utterance and a secondhypothesis of a second utterance and the system accepts the secondhypothesis, wherein the two hypotheses do not match word-for-word, butthe hypotheses mostly match word-for-word. The approach may beimplemented as an apparatus which includes a processor adapted toidentify when the system generates a first hypothesis of a firstutterance and a second hypothesis of a second utterance and the systemaccepts the second hypothesis, wherein the two hypotheses do not matchword-for-word, but the hypotheses mostly match word-for-word.

In another approach, a method for identifying a possible error made by aspeech recognition system includes identifying when a hypothesisgenerated by the system does not match an expected responseword-for-word, but the hypothesis mostly matches the expected responseword-for-word. The approach may be implemented as an apparatus whichincludes a processor adapted identify when a hypothesis generated by thesystem does not match an expected response word-for-word, but thehypothesis mostly matches the expected response word-for-word.

Still in another approach, a method for adapting a model for a speechrecognition system includes generating a count of occurrences of when auser provides feedback to the system. The method may further includeadjusting adaptation of the model based on the count. The approach maybe implemented as an apparatus which may include all or a subset of thefollowing: a processor adapted to generate a count of occurrences ofwhen a user provides feedback to the system; and a controller thatadjusts an adaptation of the model based on the count.

Example Embodiments of Speech Recognition Systems

FIG. 1A illustrates a perspective view of a user using a portableterminal 10 and headset 16, according to an example embodiment of theinvention. Portable terminal 10 may be a wearable device, which may beworn by a user 11 such as on a belt 14 as shown. Use of the term“portable terminal” herein is not limited and may include any computer,device, machine, or system which is used to perform a specific task.Portable terminal 10 may comprise processing circuitry, including aprocessor for controlling the operation of the portable terminal andother associate processing circuitry. (The processing circuitry mayimplement one or more exemplary embodiment speech recognition methodsdisclosed herein.) Headset 16 may be coupled to the portable terminal bya cord 18 or by a wireless connection (not shown in FIG. 1A) and theheadset is worn on the head of the user 11. (In another exemplaryembodiment, the speech recognition system is located in headset 16,eliminating the need for portable terminal 10.) A user can speak in aspoken language, through a microphone in the headset 16 and the audioinformation is processed by the speech recognition system in portableterminal 10. U.S. patent application Ser. No. 10/671,142, entitled“Apparatus and Method for Detecting User Speech”, incorporated herein byreference, provides further details for implementing such a system.Applications for example embodiments of the invention are not strictlylimited to the warehouse environment and portable terminals 10 shown inFIG. 1A. Other applications and environments in which exampleembodiments may be implemented are described in the BACKGROUND sectionof this application.

FIG. 1B illustrates a schematic view of a speech recognition system,according to an example embodiment of the invention. One of ordinaryskill in the art will recognize that the various functional blocks ofthe speech recognition system, including the inventive features, can beimplemented using a variety of different technologies. The invention canbe implemented into various different hardware and softwareconfigurations. One particular application for the invention is within aportable or wearable terminal that is used for voice-directed work.However, other implementations are possible as well and the invention isnot limited to such voice-directed work applications. Furthermore, toimplement various features of the inventive embodiment, the speechrecognition software might be modified. Alternatively, a separate modulemight be utilized to modify the speech recognition system according toaspects of the invention.

Referring to FIG. 1B in system 100, a speech signal, such as from asystem user, may be captured by a speech input device 102 in a varietyof conventional ways. Typically, a microphone or otherelectro-acoustical device senses speech input from a user and convertsit into an analog voltage signal 103 that then is forwarded to a signalprocessor 104. As is conventionally known, the signal processor 104includes the necessary analog-to-digital converters, filters, andequalization circuitry and/or software that convert the analog speechinput 103 into a digitized stream of data 105 that can be separated intoseparate units for analysis. Alternatively, this audio data from device102 can be retrieved from a data storage device. As discussed herein,the system 100 might be realized by suitable hardware and/or software.As such, the blocks shown in FIG. 1B are not meant to indicate separatecircuits or to be otherwise limiting, but rather show the functionalcomponents of the system.

In particular, the signal processor 104 divides the digital stream ofdata that is created into a sequence of time-slices, or frames 105, eachof which is then processed by a feature generator 106, thereby producingfeatures (vector, matrix, or otherwise organized set of numbersrepresenting the acoustic features of the frames) 107. Furtherexplanation of an example speech recognition system is provided in U.S.Pat. No. 4,882,757, entitled “Speech Recognition System”, the disclosureof which is incorporated herein by reference in its entirety. Thisreferenced patent discloses Linear Predictive Coding (LPC) coefficientsto represent speech; however, other functionally equivalent methods arecontemplated within the scope of the invention as well.

A speech recognition search algorithm function 108, realized by anappropriate circuit and/or software in the system 100 analyzes thefeatures 107 in an attempt to determine what hypothesis to assign to thespeech input captured by input device 102. As is known in the art in onerecognition algorithm, the recognition search 108 relies onprobabilistic models provided through 122 from a library of suitablemodels 110 to recognize the speech input 102. Each of the models in thelibrary 110 may either be customized to a user or be generic to a set ofusers.

When in operation, the search algorithm 108 (which can be implementedusing Hidden Markov Models with a Viterbi algorithm or other modelingtechniques such as template matching dynamic time warping (DTW) orneural networks), in essence, compares the features 107 generated in thegenerator 106 with reference representations of speech, or speechmodels, in library 110 in order to determine the word or words that bestmatch the speech input from device 102. Part of this recognition processis to assign a confidence factor for the speech to indicate how closelythe sequence of features from the search algorithm 106 matches theclosest or best-matching models in library 110. As such, a hypothesisconsisting of one or more vocabulary items and associated confidencefactors 111 is directed to an acceptance algorithm 112, which also cantake as inputs a threshold adjustment 116 and one or more expectedresponses 114. If the confidence factor is above a predeterminedacceptance threshold (or an adjusted threshold when the hypothesismatches the expected response), then the acceptance algorithm 112 makesa decision 118 to accept the hypothesis as recognized speech. If,however, the confidence factor is not above the acceptance threshold, asutilized by the acceptance algorithm, then the acceptance algorithm 112makes a decision 118 to ignore or reject the recognized speech. Thesystem may then prompt the user to repeat the speech. In this instance,the user may repeat the speech to input device 102. The hypothesis andconfidence factors 111, the expected response 114, acceptance algorithmdecision 118 and features 107 can also be input to a model adaptationcontrol module 117. Model adaptation control module 117 (which may byimplemented in a hardware or software controller or control mechanism)controls the adaptation of library of models 110.

An Example Embodiment Model Adaptation Control Module

FIG. 2 illustrates a model adaptation control module 200, an exampleembodiment implementation of the model adaptation control module 117 inFIG. 1B. Error rate module 210 (which may be implemented in a processor)determines or estimates an error rate of the speech recognition system.The error rate may be a word error rate, which can be defined as thepercentage or ratio of speech recognition errors over the number ofwords input to the system, and the error rate can be determined orestimated over a window of time (e.g. predetermined length of time)and/or data (e.g. predetermined number of utterances input to thesystem). (An utterance is a spoken phrase of at least one word such as‘1’ or “1-2-3”.) Furthermore, the error rate can be an error ratedetermined or estimated in the following ways: per user; over a numberof users; per word; over a set of words; or per a group of consecutivelyspoken words, such as an utterance, phrase or sentence. Furthermore, theerror rate determined by module 210 can be based on actual errorsdetermined from comparing the system's hypothesis to the referencetranscript or based on estimated errors deemed to have occurred afterevaluating system and user behavior, as discussed later in thisapplication. Therefore, the error rate determination may also be anerror rate estimation. Inputs 205 to error rate module 210 are thoseneeded for an error rate determination or estimation used for aparticular application. In this example embodiment, inputs 205 are ahypothesis and confidence factor (such as 111 of FIG. 1B) with itsassociated timing information and expected response(s) (such as 114 ofFIG. 1B). Error rate module 210 outputs an error rate 215 to adaptationcontrol module 225.

Adaptation control module 225 controls or adjusts the adaptation ofmodels by model adaptation module 235. Inputs 220 are those needed forthe control of model adaptation desired for a particular application. Inthis example embodiment, inputs 220 are a hypothesis and features (suchas 107 of FIG. 1B). Adaptation control module 225 outputs instructions230 to model adaptation module 235. In this example embodiment,instructions 230 can include instructions of when to adapt a certainmodel or models (including instructions to adapt or withholdadaptation), which utterances to use to adapt the models (including thetranscription of the utterance and the features observed by therecognition system corresponding to the utterance). By providing controlinstructions 230, adaptation control module 225, can control whenadaptation should occur and determine the criteria to be met beforeadaptation is ordered. Furthermore, once adaptation is to proceed, theapplication or adaptation control module may determine whether theexisting models are replaced with new models created with the newfeatures (or features of new examples of words) only or whether theexisting models are just adapted using information from both the newfeatures and the existing features of the existing models. Modeladaptation module 235 outputs adapted models 240 to the library (such as110 of FIG. 1B) of models.

Because it is useful to prevent the system from adapting in anundesirable way, thereby resulting in a system that performs worse thanit did prior to adaptation or a system that degrades over time, andbecause it is extremely useful to use resources only when necessary, inone embodiment, an adaptation control module (such as 225 of FIG. 2)uses an error rate (such as 215 of FIG. 2) to control or adjust theadaptation (including adapting or withholding adaptation) of models (byfor example model adaptation module 235 of FIG. 2).

Example Embodiment Methods for Controlling Model Adaptation

FIG. 3 is a flow chart illustrating a method 300 for controlling oradjusting model adaptation, according to an example embodiment of theinvention. It can be executed by a component of a speech recognitionsystem, such as model adaptation and control module 117 of FIG. 1B. At305, input speech is received by the speech recognition system from, forexample, a user or a recording from a user's session. At 310, initialspeech processing is performed (such as processing of the input speechperformed by the signal processor 104, feature generator 106 and speechrecognition search algorithm 108 of FIG. 1B) for words input to thesystem. At 315, an error rate, corresponding to either recognition ofinstances of a word or for recognition of the instances of variouswords, is determined or estimated. For example, the error rate can bebased on recognition errors for the word ‘1’, for the words ‘1’, ‘2’ and‘3’, for all digits, or for all words in the vocabulary of the system.The error rate can be an updated error rate based on of instancespreviously and currently input to the system. At 320, a determination ismade whether to adapt (such as by the processing of the model adaptationcontrol module 117 of FIG. 1B) a model for the word or various modelsfor the various words, based on the error rate. For example, adetermination can be made to adapt the model for the word ‘1’ based onan error rate for the word ‘1’. In another example, a determination canbe made to adapt all words that are digits, based on a combined errorrate for all of the digits. If it was determined that the model(s)should not be adapted, next is 305. If the model(s) should be adapted,the model(s) are adapted in 325. After 325 is executed, control returnsto 305. Model adaptation in 325 can be performed in the background withcontrol returning to 305 immediately. In other words, the speechrecognition system can continue to receive and process speech while themodels are being adapted.

In an example embodiment, an error rate is compared to a predeterminedcriterion such as an error rate threshold to control model adaptation.In other words, an example embodiment makes a comparison of an errorrate to an error rate threshold and adapts at least one model orwithholds adapting the model based on the comparison. For example, ifthe error rate 215 is below a particular error rate threshold, anadaptation control module (such as 225 in FIG. 2) provides instructionsto model adaptation module 235 to withhold adapting the model(s)associated with the error rate determination or estimation. One reasonbehind the instructions to adapt is that if the error rate is below aparticular error rate threshold, the speech recognition system isperforming well and accordingly, model adaptation should not beperformed. If the error rate is above a particular error rate threshold,the adaptation control module provides instructions to a modeladaptation module to perform model adaptation. The instructions caninclude performing model adaptation only on models associated with theerror rate determination or estimation.

In example embodiments of the invention, the error rate threshold can bea predetermined value, a value settable by a user, a dynamic value, orit can be adjusted upwardly or downwardly. Moreover, the error ratethreshold can be based on factors that affect the achievable error rateof the speech recognition system and those that determine an acceptableerror rate for the application in which the system is used. Furthermore,the error rate threshold can be based on a number of words in anutterance input to the speech recognition system (or number of words inthe system's hypothesis of an utterance), based on environmental factors(such as background noise level or a signal-to-noise ratio), based onthe perplexity of the grammar of a speech recognition system, based ongrammar complexity or confusability of the words in the vocabulary, anyother measure of difficulty of performing a speech recognition task, orbased on a number of words in the vocabulary of a speech recognitionsystem.

Example Embodiments of Error Rates

Throughout this present application, there are various exampleembodiments for determining or estimating the occurrences of possible(or potential or suspected) errors made by a speech recognition systemand an error rate (which can be performed by the error rate module 210of FIG. 2 and 315 of FIG. 3. The error rate can be used to control oradjust adaptation by the adaptation and control module 225 of FIG. 2 and320 of FIG. 3). The error rate can be based on any one or combination ofthe various speech recognition errors discussed in this presentapplication, such as those in the BACKGROUND section of this presentapplication and those discussed below. For example, the error rate canbe the ratio of insertion errors over words input to the system. Or forexample, the error rate can be the ratio of insertion, substitution anddeletion errors over the words input to the system. Or for example, theerror rate can be the combination of the low confidence rate and thesubstitution rates discussed below. The exemplary embodiment error ratesdiscussed below are based on estimated errors which are deemed to haveoccurred based on evaluating system behavior, the expected responseand/or user behavior. Thus, these estimated error rates provide anadvantage of not requiring a reference transcript of words input to thesystem and comparison of the system's hypotheses corresponding to thewords input to the system.

Low Confidence Rate

In an example embodiment of the invention, a count of occurrences ofpossible errors made by a speech recognition system can be used todetermine an estimate of a low confidence rate or an estimate of anerror rate. FIG. 4 is a flow chart illustrating a method 400 foridentifying errors, which can be executed by components of a speechrecognition system, such as the error rate module 210 of FIG. 2. The lowconfidence rate is the rate at which a word is recognized with aconfidence factor within a certain range corresponding to low confidencethat the system recognized the word correctly. In other words, the lowconfidence rate is the frequency at which a word was recognized by thespeech recognition system with a confidence factor that is relativelylow depending on the application in which the speech recognition systemis used. Note that a low confidence rate does not measure errors by thespeech recognition system, but the low confidence rate (or afraction/multiple of its value) can be used in addition to or in placeof error rate estimates where error rates (or error rate estimates) areused.

In FIG. 4, at 405, the confidence factor for a hypothesized word isdetermined. (This confidence factor can be generated by search algorithm108 of FIG. 1B and supplied to the error rate module 210 of FIG. 2.) At410, the confidence factor is compared with a range of valuescorresponding to low confidence that the system recognized the wordcorrectly for the application in which the system is used. If at 410 itis determined that the confidence factor is outside of the lowconfidence range, control is returned to 405. If it is determined at 410that the confidence factor is within the low confidence range, the errorcount is incremented at 415. After 415, control returns to 405. Theerror count in 415 may then be combined with counts of other error typesto generate an error rate.

An exemplary embodiment, which uses a low confidence rate, alsoconsiders when a word is from a hypothesis generated by the system thatmatches an expected response in counting errors for an error rateestimation. (U.S. patent application Ser. No. 11/051,825, and theBACKGROUND section of this present application describes scenarios inwhich an expected response from a user is processed by a speechrecognition system. An expected response can be defined as a responsethat the system expects to receive from the user, as a result of theapplication in which the system is used). In an example embodiment inthe referenced patent application, an acceptance algorithm of the systemnormally requires that the system's hypothesis is accepted only if aconfidence factor for the hypothesis exceeds an acceptance threshold.However, when the system's most likely hypothesis matches an expectedresponse, the hypothesis is more favorably treated so that thehypothesis may be accepted by the system. The reasoning behind thefavorable treatment despite the relatively low confidence factor is thata hypothesis matching an expected response usually indicates a highprobability of correct recognition.

Turning back to the example embodiment of the present invention, inwhich the error rate is a low confidence rate, responses that match theexpected response and have a relatively low confidence factor for theapplication in which the system is used are counted as errors for anerror rate estimation. Although a recognition error may not haveactually occurred (because the system's hypothesis was correctlyaccepted due to the hypothesis matching the expected response asdescribed in referenced U.S. patent application Ser. No. 11/051,825), inthis example embodiment, a word with a relatively low confidence iscounted as an error for an error rate estimation due to the relativelylow confidence factor. The range of confidence factors for which a wordis counted as a low confidence could be, for example, between theadjusted acceptance threshold and the original, unadjusted acceptancethreshold. More generally, the confidence factor thresholds or range forthe counting low confidence errors do not need to match the acceptancethreshold and adjusted acceptance threshold in the referenced patentapplication. The range could be between two other thresholds, includinga high confidence threshold, which is higher than the acceptancethreshold and indicates the boundary between low and high confidence. Inthis example embodiment, the range of confidence factors used for thelow confidence rate is determined based on the application in which thespeech recognition system is used.

Substitution Rate

In an example embodiment of the invention, a count of occurrences ofpossible substitution errors made by a speech recognition system can beused to determine an estimate of a substitution rate or an estimate ofan error rate. The substitution rate is the rate at which substitutionerrors (such as the substitution errors defined in the BACKGROUNDsection of this present application) are made by a system. In anexemplary embodiment, a hypothesis generated by the speech recognitionsystem is compared to an expected response and a substitution erroroccurs if the system replaces a word in the expected response with anincorrect word in the hypothesis. For example, if the system recognizes“1-5-3” and the expected response is “1-2-3”, a substitution error iscounted because it is deemed that the system made one substitution:substituting the ‘5’ for the ‘2’. In other words, if the hypothesis andthe expected response do not match word-for-word, but do mostly match(i.e. the hypothesis and the expected response match except for apredetermined number of words), it is a reasonable assumption that aword substitution error has occurred. (The predetermined number of wordsdepends upon the application. For example, an application that usesthree-word hypotheses or utterances may define “mostly match” asmatching word-for-word except for one word. An application that usesfive-word hypotheses or utterances may define “mostly match” as matchingword-for-word except for two words.)

Repeated Utterances

Yet in another example embodiment, the error rate is based on arecognition error made by the speech recognition system that is realizedafter comparing the system's decision on its hypotheses of at least twoconsecutive or proximate utterances. The decision can occur after thespeech recognition system has processed the incoming utterances (such asat 118 of FIG. 1B, after the acceptance algorithm in 112 of FIG. 1B isexecuted). The recognition error can be for example to reject thesystem's hypothesis of an incoming utterance, after which the userrepeats the utterance, in response to the system's response or lack ofone. Or for example, the recognition error can be to substitute a wordthat the speech recognition system is unable to recognize correctly,with another word or “garbage” word, in the speech recognition systemoutput. FIGS. 5-6 illustrate example embodiment methods to estimatethese types of error rates.

Reject and Repeat

FIG. 5 is a flow chart illustrating a method 500 of an exemplaryembodiment for identifying possible occurrences of errors made by aspeech recognition system. The count of the possible occurrences oferrors can be used to determine an estimate of an error rate. Method 500can be executed by a component of a speech recognition system, such aserror rate module 210 of FIG. 2. In this embodiment, the determinationof whether the speech recognition system made an error is made when thespeech recognition system receives at least two consecutive or proximateutterances. The system and user behavior is as follows: the systemrejects its hypothesis of the first utterance; the user repeats thefirst utterance in the second utterance; and the system accepts itshypothesis of the second utterance. The first and second hypothesesgenerated by the system substantially match. In other words, thehypotheses match word-for-word but a hypothesis may also include arecognized model that is considered to be negligible for this particularerror determination. For example, a hypothesis could include arecognized model indicating a user's breath or sigh and these recognizedmodels can be considered negligible for this particular errordetermination. However, recognized models in a hypothesis that indicatethe system is having difficulty discerning what the user spoke (such asfor example a recognized model indicating silence, garbage or a wildcard word) might not be considered negligible. (The determination ofwhether a recognized model is negligible depends upon the particularspeech recognition system and the application in which it is used.) Anexample is as follows: a user speaks a first utterance “1-2-3”; thesystem correctlyrecognizes it (i.e. generates a hypothesis of “1-2-3”)but rejects its hypothesis because of a low confidence factor; the userrepeats “1-2-3” in a second utterance and the system correctlyrecognizes it (i.e. generates a hypothesis of “1-2-3”) and accepts itshypothesis. A rationale behind this type of error detection mechanism isthat if the two matching utterances are spoken consecutively, and thesystem accepts its hypothesis of the second utterance, one couldreasonably assume that the system should have accepted its hypothesis ofthe first utterance and that it erred in not doing so. This heuristiccan alternatively require that the two utterances are spoken within apredetermined amount of time of each other, or further refined by alsorequiring that the utterances are spoken consecutively.

In FIG. 5, at 505, decisions made by a speech recognition system (suchas at 118 of FIG. 1B) on a first and second utterance are received forprocessing by a model adaptation and control module (such as 117 of FIG.1B). At 510, verifications are performed. These verifications caninclude one or more of the following conditions: verifying that thesystem's hypotheses of those utterances contain multiple words;verifying that the system's hypothesis of the second utterance containsall accepted words; verifying that there was at least one rejected wordin the system's hypothesis for the first utterance; and verifying thatthe second hypothesis matches the expected response (if there is one).At 515, if the verifications pass, next is 520. Otherwise, controlreturns to 505. At 520, the words in the first and second hypotheses arecompared word-for-word to find if they match. For example, if the firsthypothesis is “one-two-three” and the second hypothesis is“one-three-three”, there is a mismatch. If the hypotheses matchword-for-word, there is a high probability that an incorrect rejectionerror has occurred, with the reasoning that the user repeated himselfand the system recognized the second utterance correctly. If thehypotheses match word-for-word, next is 525. Otherwise, control returnsto 505. At 525, the error count is incremented and control returns to505. The error count in 525 may then be combined with counts of othererror types to generate an overall error rate.

Substitute and Repeat

FIG. 6 is a flow chart illustrating a method 600 of an exemplaryembodiment for identifying possible occurrences of errors made by aspeech recognition system. The count of the possible occurrences oferrors can be used to determine an estimate of an error rate. Method 600can be executed by a component of a speech recognition system, such aserror rate module 210 of FIG. 2. In this embodiment, the determinationof whether the speech recognition system made an error is made when thespeech recognition system receives at least two consecutive or proximateutterances and the system substitutes a word in its hypothesis of thefirst utterance and recognizes and accepts all of the words in itshypothesis of the second utterance. An example is as follows: a userspeaks a first utterance “1-2-3”; the system misrecognizes it (e.g.generates a hypothesis “1-5-3”) and accepts its hypothesis; the userrepeats “1-2-3” in a second utterance within a proximity of the firstutterance; the system correctly recognizes it (i.e. generates ahypothesis “1-2-3”) and accepts its hypothesis. A rationale behind thismethod of detecting errors is that if the two utterances are spokenconsecutively or within a proximity of each other, and if the systemaccepts its hypothesis of the second utterance, then the system likelymade a substitution in its hypothesis of the first utterance. There areheuristics that may be used to guard against the system consideringconsecutive or proximate recognitions differing by a single word ascontaining a substitution error when in fact they do not. The heuristicsinclude checking for one or more of the following possible conditions:there were no intervening utterances that indicate that the firstutterance was correctly recognized by the system; the two utterancesbeing compared represent the same piece of information being enteredinto the system, for example, the two utterances being compared occurredat the same position in the dialogue between the user and therecognition system, or in response to the same prompt; the twoutterances were spoken within a predetermined amount of time or in otherwords the time between the two utterances being compared was shortenough to indicate that the user was repeating the initial utterance.

In FIG. 6, at 605, decisions made by a speech recognition system (suchas at 118 of FIG. 1B) on a first and second utterance are received forprocessing by a model adaptation and control module (such as 117 of FIG.1B). At 610, verifications are performed. These verifications improvethe accuracy of the estimate of the substitution error rate and mayinclude: verifying that the utterances were spoken consecutively orwithin a proximity of each other; verifying that the system's hypothesesof the utterances contain multiple words; verifying that the system'shypotheses of the utterances contain all accepted words; verifying thatthe user was prompted for the same information by the system both times;verifying that the first hypothesis does not match the expected response(if there is one); and verifying that the second hypothesis does matchthe expected response (if there is one); and checking for a conditionindicating a substitution error occurred (such as those describedabove). At 615, the words in the system's hypotheses of the first andsecond utterances are compared word-for-word to see if they match. Ifthe hypotheses do not match word-for-word, next is 620. Otherwise,control returns to 605. At 620, if the verifications pass, next is 625.Otherwise, control returns to 605. At 625, the words in the system'shypotheses of the first and second utterances are compared word-for-wordto find how closely they match. For example, if the first hypothesis is“1-2-3” and the second hypothesis is “1-5-3”, there is a mismatch of oneword. In this case, the ‘5’ was substituted for the ‘2’. If thehypotheses do not match word-for-word, but do mostly match, (e.g. thehypotheses match except-for one word), it is a reasonable assumptionthat a word substitution error has occurred, with the reasoning that thesystem performed verifications such as checking for at least onecondition indicating a substitution error occurred, the user repeatedthe same utterance, the system recognized the second utterancecorrectly, and the system incorrectly substituted a word in itshypothesis of the first utterance. (The definition of “mostly match”depends upon the application. For example, an application that usesfive-word hypotheses or utterances may define “mostly match” as matchingword-for-word except for two words.) If the hypotheses mostly matchword-for-word, next is 630 where the error count is incremented followedby control returning to 605. The error count in 630 may then be combinedwith counts of other error types to generate an overall error rate.

The same approach as in the previous paragraph can be used to detectdeletion due to garbage errors where a content word is recognized by thesystem as garbage in a first utterance, then correctly recognized in thenext utterance. By comparing the recognition results of the twoutterances and using verifications such as those described above, onecan detect the error. For example, if the system's hypothesis of thefirst utterance is “1-GARBAGE-3” and the system's hypothesis of thesecond utterance is “1-5-3”, there is a mismatch of one word, and itbecomes a reasonable assumption that the speech recognition system madean error in its hypothesis of the first utterance. Again, similarverifications as described above may be used to guard against the systemconsidering a correct recognition to be in error.

The same approach as described above in the discussion of FIG. 6 canalso be used to detect other types of errors, such as a deletion due torejected substitution error. An example of a deletion due to rejectedsubstitution error is as follows. A user speaks a first utterance“1-2-3” and the system recognizes it (i.e. generates a hypothesis“1-2-3”), but the system rejects the ‘2’ in its hypothesis. The userspeaks a second utterance “1-5-3” within a proximity of the firstutterance and the system recognizes it (i.e. generates a hypothesis“1-5-3”) and the system accepts its hypothesis. When the system rejectedthe ‘2’ in its hypothesis of the first utterance, the system made adeletion due to rejected substitution error. The method for detectingthis type of error is similar to that described in the discussion ofFIG. 6, with the difference that the system's hypothesis of the firstutterance does not need to contain all accepted words.

Correction Rate

In an exemplary embodiment error rate for a speech recognition system, acount of occurrences of when a user provides feedback to the system canbe used as an estimate of an error rate or an estimate for part of anerror rate. The reasoning behind using a such a count to estimate of anerror rate or estimate part of an error rate is that when a correctionis commanded to the system, it may indicate that an error occurred.Examples of user feedback are described in the BACKGROUND section ofthis present application. The count can include the rate at which theuser indicates that the system made a mistake. Furthermore, the user mayprovide feedback in response to the system requesting feedback, such asasking the user to confirm a hypothesis generated by the system orasking the user to identify what word was spoken by the user. Thefeedback may include a word indicating aggravation by the user or thefeed back may be a correction command to the system, such as “back-up”or “erase”. In determining or estimating the error rate, considerationscan be made for the amount of time and data needed to determine orestimate an error rate that is useful for the application in which thespeech recognition system is used. One example consideration is that theerror rate is determined or estimated for speech input to the speechrecognition system over a predetermined length of time. Another exampleconsideration is that the error rate is determined or estimated forspeech input to the speech recognition system over a predeterminednumber of utterances, words, or hypotheses.

Error Rate Considerations

Another example consideration is that the error rate is determined orestimated from hypotheses of utterances collected over a moving orsliding window or a collection period that is dynamic in length of timeand/or size of data. As a result, the error rate is determined orestimated over a period when useful data has been collected. Forexample, a moving or sliding window can cover a collection of data takenfrom equal periods in noisy environment and a quiet environment tooffset any favoring by the speech recognition system in one of thoseenvironments. Other examples of moving, sliding windows are those thatcollect data only during recent use (e.g. the last half-hour) of thespeech recognition system, collecting data for time spent by aparticular user (e.g. since the user started using the system), orcollecting a certain amount of data regardless of the time spentcollecting the data (e.g. the last one-hundred hypotheses).

It can be understood by those skilled in the art that in other exemplaryembodiments of the invention, other error rates can be used, such as aphrase error rate, utterance error rate, and sentence error rate. Forexample, an utterance error rate can be defined as the percentage orratio of speech recognition errors over the number of utterances inputto the system and the utterance error rate can be used in controlling oradjusting an adaptation of a model.

Furthermore, it will be evident to one skilled in the art that thevarious methods to identify possible errors can process the samehypotheses, but safeguards must be taken to avoid double counting, assome possible errors may be counted by more than one method.

Example Embodiment of Model Adaptation

FIG. 7 is a flow chart illustrating a method 700 for model adaptation,according to an example embodiment of the invention. It can be executedby a component of a speech recognition system, such as the modeladaptation module 235 of FIG. 2. In an example embodiment, method 700 isexecuted under the control of a control module (such as 220 of FIG. 2).In other words, method 700 is performed based on instructions from acontrol module. The instructions can include instructions of when toadapt a certain model or multiple models (including instructions toadapt or withhold adaptation) and information corresponding to themodel(s) to be adapted, such as transcription of an utterance and a setof features observed by the speech recognition system corresponding tothe utterance.

At 705, the features observed by a speech recognition systemcorresponding to an input utterance are aligned with the states in themodels for the words of the utterance. In an example embodiment, theBaum-Welch re-estimation algorithm can be used to perform the alignment.At 710, the statistics (for example, means and variances) of the statesare updated using the values of the features. At 715, these values aremixed into the models with an appropriate weighting to maintain abalance between previous training data and new features. Thus, in anexample embodiment of the invention, new models are created throughadaptation by using the observed features of an input utterance to adaptexisting or original models. In that scenario, both the observedfeatures of the input utterance and the existing features of theoriginal models, and the statistics associated with each, are used tocreate the new models. Additionally, in such a scenario, the newstatistics might be weighted in various fashions to tailor their effecton the original statistics in the model. In an alternative exampleembodiment of the invention, only the new observed features (of newexamples of words), and information therefrom, are utilized to createthe new model. That is, a new model is created with the new featuresonly. Furthermore, the adaptation could be performed using data from asingle user or multiple users. In one particular embodiment, only speechdata from an individual user might be used to perform the adaptation.This generates a model that is adapted for that user and performs wellfor that user.

FIGS. 3-7 show flow charts illustrating methods according to embodimentsof the invention. The techniques illustrated in these figures may beperformed sequentially, in parallel or in an order other than that whichis described. It should be appreciated that not all of the techniquesdescribed are required to be performed, that additional techniques maybe added, and that some of the illustrated techniques may be substitutedwith other techniques.

The invention in its various forms may be implemented directly in thesoftware of a speech recognition system. That is, the improvements areactually part of the speech recognition system. Alternatively, theinvention does not have to be built into the speech recognition system.Rather, the invention or parts of the invention may be implemented in aseparate module, program or application which interacts with a speechrecognition system to provide the benefits of the invention. Forexample, a separate application or software module may be utilized tohandle the adaptation in accordance with the principles of theinvention. Specifically, an application may interface with a speechrecognition system to determine or estimate an error rate and/or controlwhen and how models are adapted.

In the foregoing description, the invention is described with referenceto specific example embodiments thereof. The specification and drawingsare accordingly to be regarded in an illustrative rather than in arestrictive sense and it is not the intention of the applicants torestrict or in any way limit the scope of the appended claims to suchdetail. It will, however, be evident to those skilled in the art thatadditional advantages and modifications can be made, in a computerprogram product or software, hardware or any combination thereof,without departing from the broader spirit and scope of the inventionwill readily appear. Software embodiments may include an article ofmanufacture on a machine accessible or machine readable medium havinginstructions. Furthermore, software embodiments may be distributed ordownloaded via a network or email. The instructions on the machineaccessible or machine readable medium may be used to program a computersystem, such as for example, a PC, cell phone, industrial mobilecomputer, PDA, electronic headset or other electronic device withexemplary embodiment methods or approaches disclosed herein. Themachine-readable medium may include, but is not limited to non volatilememory, floppy diskettes, optical disks, CD-ROMs, and magneto-opticaldisks or other type of media/machine-readable medium suitable forstoring or transmitting electronic instructions. Furthermore, departuresmay be made from the application in which the invention is describedwithout departing from the spirit and scope of the invention. Forexample, the example speech recognition system described herein hasfocused on wearable terminals. However, the principles of the inventionare applicable to other speech recognition environments as well.

1. A method for model adaptation for a speech recognition systemcomprising: determining an error rate, corresponding to eitherrecognition of instances of a word or recognition of instances ofvarious words among a set of words; and adjusting an adaptation of amodel for the word or various models for the various words, based on theerror rate.
 2. The method of claim 1, wherein the error rate is based onactual errors determined from comparing a transcript of words input tothe system with corresponding hypotheses generated by the system.
 3. Themethod of claim 1, wherein the error rate is based on estimated errorsestimated from evaluating system and user behavior corresponding toproximate utterances input to the system by the user.
 4. The method ofclaim 1, wherein adjusting an adaptation comprises adapting the model orthe various models or withholding adapting the model or the variousmodels based on the error rate.
 5. The method of claim 1, whereinadjusting the adaptation comprises: making a comparison of the errorrate to an error rate threshold; and adapting the model or the variousmodels or withholding adapting the model or the various models based onthe comparison.
 6. The method of claim 1, wherein adjusting theadaptation comprises: withholding adapting the model or the variousmodels until the error rate meets an error rate threshold.
 7. The methodof claim 5, wherein the error rate threshold is a predetermined value.8. The method of claim 5, wherein the error rate threshold is a valuesettable by a user.
 9. The method of claim 5, wherein the error ratethreshold is a dynamic value.
 10. The method of claim 5, wherein theerror rate threshold is based on a number of words in a hypothesis of anutterance input to the system.
 11. The method of claim 5, wherein theerror rate threshold is based on an environmental factor.
 12. The methodof claim 5, wherein the error rate threshold is based on a measure ofdifficulty of the recognition of the word.
 13. A method for identifyinga possible error made by a speech recognition system, comprising:identifying an instance of a word that was recognized by the systemwithin a certain confidence factor range.
 14. The method of claim 13,wherein a count of occurrences of the possible error is used inadjusting an adaptation a model for the word associated with thepossible error.
 15. The method of claim 13, wherein the word is from ahypothesis generated by the system that matches an expected response.16. The method of claim 13, wherein the range comprises valuescorresponding to low confidence that the system recognized the instancesof the word correctly for an application in which the system is used.17. The method of claim 13, wherein the range is a predetermined set ofvalues in between a low confidence threshold and a high confidencethreshold.
 18. The method of claim 17, wherein the low confidencethreshold is an adjusted acceptance threshold, which is a value lowerthan the acceptance threshold.
 19. The method of claim 17, wherein thehigh confidence threshold is a value equal to or higher than anacceptance threshold for accepting a hypothesis generated by the system,in which a confidence value associated with the hypothesis must exceedthe acceptance threshold in order for the hypothesis to be accepted. 20.A method for identifying a possible error made by a speech recognitionsystem comprising: identifying an instance where the system rejects afirst hypothesis of a first utterance, followed by the system acceptinga second hypothesis of a second utterance, wherein the first and secondhypotheses substantially match word-for-word.
 21. The method of claim20, wherein the first and second hypotheses substantially matchword-for-word by matching word-for-word, except that one of the firstand second hypotheses includes at least one additional recognized modelthat is negligible for purposes of identifying the possible error. 22.The method of claim 20, wherein a count of occurrences of the possibleerror is used in adjusting an adaptation of a model for a wordassociated with the possible error.
 23. The method of claim 20, whereinthe system rejects the first hypothesis due to a confidence factor ofthe first hypothesis not exceeding an acceptance threshold.
 24. Themethod of claim 20, wherein the system rejects at least one word in thefirst hypothesis.
 25. The method of claim 20, wherein the first andsecond utterances are spoken consecutively, proximately or within apredetermined amount of time.
 26. A method for identifying a possibleerror made by a speech recognition system comprising: identifying whenthe system generates first and second hypotheses of two utterances andthe system accepts the second hypothesis, wherein the two hypotheses donot match word-for-word, but the hypotheses mostly match word-for-word.27. The method of claim 26, wherein the hypotheses mostly matchword-for-word by matching word-for-word except for a predeterminednumber of words.
 28. The method of claim 26, wherein a count ofoccurrences of the possible error is used in adjusting an adaptation amodel for a word associated with the possible error.
 29. The method ofclaim 26, wherein the two utterances are spoken consecutively,proximately, or within a predetermined amount of time.
 30. The method ofclaim 26, wherein there are no intervening utterances that indicate thatthe first of the two utterances was correctly recognized by the system.31. The method of claim 26, wherein the two hypotheses differ in that aword in the second hypothesis is substituted with a word in the firsthypothesis.
 32. The method of claim 26, wherein the hypotheses differ inthat a word in the second hypothesis is substituted with garbage in thefirst hypothesis.
 33. A method for identifying a possible error made bya speech recognition system, comprising: identifying when a hypothesisgenerated by the system does not match an expected responseword-for-word, but the hypothesis mostly matches the expected responseword-for-word.
 34. The method of claim 33, wherein a count ofoccurrences of the possible errors is used in adjusting adaptation of amodel for a word associated with the possible errors.
 35. The method ofclaim 33, wherein the hypothesis mostly matches the expected responseword-for-word by matching word-for-word except for one word.
 36. Amethod for adapting a model for a speech recognition system comprising:generating a count of occurrences of when a user provides feedback tothe system; and adjusting an adaptation of the model based on the count.37. The method of claim 36, wherein the count is determined withoutusing a transcript of words input to the system.
 38. The method of claim36, wherein the count of occurrences includes a count of occurrences ofwhen the user indicates that the system made a mistake.
 39. The methodof claim 36, wherein the user provides the feedback in response to thesystem requesting the feedback.
 40. The method of claim 39, wherein thefeedback includes a word or other indication of aggravation by the user.41. The method of claim 39, wherein the feedback is a correction commandto the system.
 42. An apparatus for model adaptation for a speechrecognition system comprising: a processor adapted to determine an errorrate, corresponding to either recognition of instances of a word orrecognition of instances of various words among a set of words; and acontroller adapted to adjust an adaptation of a model for the word orvarious models for the various words, based on the error rate.
 43. Theapparatus of claim 42, wherein the error rate is based on actual errorsdetermined from comparing a transcript of words input to the system withcorresponding hypotheses generated by the system.
 44. The apparatus ofclaim 42, wherein the error rate is based on estimated errors estimatedfrom evaluating system and user behavior corresponding to proximateutterances input to the system by the user.
 45. The apparatus of claim42, wherein the controller adjusts an adaptation by adapting the modelor the various models or withholding adapting the model or the variousmodels based on the error rate.
 46. The apparatus of claim 42, whereinthe controller adjusts the adaptation by making a comparison of theerror rate to an error rate threshold; and adapting the model or thevarious models or withholding adapting the model or the various modelsbased on the comparison.
 47. The apparatus of claim 42, wherein thecontroller adjusts the adaptation by withholding adapting the model orthe various models until the error rate meets an error rate threshold.48. The apparatus of claim 42, wherein the error rate threshold is apredetermined value.
 49. The apparatus of claim 42, wherein the errorrate threshold is a value settable by a user.
 50. The apparatus of claim42, wherein the error rate threshold is a dynamic value.
 51. Theapparatus of claim 42, wherein the error rate threshold is based on anumber of words in a hypothesis of an utterance input to the system. 52.The apparatus of claim 42, wherein the error rate threshold is based onan environmental factor.
 53. The apparatus of claim 42, wherein theerror rate threshold is based a measure of difficulty of the recognitionof the word.
 54. An apparatus for identifying a possible error made by aspeech recognition system, comprising: a processor adapted to identifyan instance of a word that was recognized by the system within a certainconfidence factor range.
 55. The apparatus of claim 54, wherein a countof occurrences of the possible error is used in adjusting adaptation ofa model for the word associated with the possible error.
 56. Theapparatus of claim 54, wherein the word is from a hypothesis generatedby the system that matches an expected response.
 57. The apparatus ofclaim 54, wherein the range comprises values corresponding to lowconfidence that the system recognized the instances of the wordcorrectly for an application in which the system is used.
 58. Theapparatus of claim 54, wherein the range is a predetermined set ofvalues in between a low confidence threshold and a high confidencethreshold.
 59. The apparatus of claim 58, wherein the high confidencethreshold is an acceptance threshold for accepting a hypothesisgenerated by the system, in which a confidence threshold associated withthe hypothesis must exceed the acceptance threshold in order for thehypothesis to be accepted.
 60. The apparatus of claim 58, wherein thelow confidence threshold is an adjusted acceptance threshold, which is avalue lower than the acceptance threshold.
 61. The apparatus of claim58, wherein the high confidence threshold is a value equal to or higherthan an acceptance threshold for accepting a hypothesis generated by thesystem, in which a confidence value associated with the hypothesis mustexceed the acceptance threshold in order for the hypothesis to beaccepted.
 62. An apparatus for identifying a possible error made by aspeech recognition system comprising: a processor adapted to identify aninstance where the system rejects a first hypothesis of a firstutterance, followed by the system accepting a second hypothesis of asecond utterance, wherein the first and second hypotheses substantiallymatch word-for-word.
 63. The apparatus of claim 62, wherein the firstand second hypotheses substantially match word-for-word by matchingword-for-word, except that one of the first and second hypothesesincludes at least one additional recognized model that is negligible forpurposes of identifying the possible error.
 64. The apparatus of claim62, wherein a count of occurrences of the possible error is used inadjusting an adaptation of a model for a word associated with thepossible error.
 65. The apparatus of claim 62, wherein the systemrejects the first hypothesis due to a confidence factor of the firsthypothesis not exceeding an acceptance threshold.
 66. The apparatus ofclaim 62, wherein the system rejects at least one word in the firsthypothesis.
 67. The apparatus of claim 62, wherein the first and secondutterances are spoken consecutively, proximately or within apredetermined amount of time.
 68. An apparatus for identifying apossible error made by a speech recognition system comprising: aprocessor adapted to identify when the system generates first and secondhypotheses of two utterances and the system accepts the secondhypothesis, wherein the two hypotheses do not match word-for-word, butthe hypotheses mostly match word-for-word.
 69. The apparatus of claim68, wherein the hypotheses mostly match word-for-word by matchingword-for-word except for a predetermined number of words.
 70. Theapparatus of claim 68, wherein a count of occurrences of the possibleerror is used in adjusting an adaptation a model for a word associatedwith the possible error.
 71. The apparatus of claim 68, wherein the twoutterances are spoken consecutively, proximately, or within apredetermined amount of time.
 72. The apparatus of claim 68, whereinthere are no intervening utterances that indicate that the first of thetwo utterances was correctly recognized by the system.
 73. The apparatusof claim 68, wherein the two hypotheses differ in that a word in thesecond hypothesis is substituted with a word in the first hypothesis.74. The apparatus of claim 68, wherein the hypothesis differ in that aword in the second hypothesis is substituted with garbage in the firsthypothesis.
 75. An apparatus for identifying a possible error rate madeby a speech recognition system, comprising: a processor adapted toidentify when a hypothesis generated by the system does not match anexpected response word-for-word, but the hypothesis mostly matches theexpected response word-for-word.
 76. The apparatus of claim 75, whereina count of occurrences of the possible error is used in adjusting anadaptation of a model for a word associated with the possible error. 77.The apparatus of claim 75, wherein the hypothesis mostly matches theexpected response word-for-word by matching word-for-word except for apredetermined number of words.
 78. An apparatus for adapting a model fora speech recognition system comprising: a processor adapted to generatea count of occurrences of when a user provides feedback to the system;and a controller adapted to adjust an adaptation of the model based onthe count.
 79. The apparatus of claim 78, wherein the count isdetermined without using a transcript of words input to the system. 80.The apparatus of claim 78, wherein the transcript is compared withcorresponding hypotheses generated by the system.
 81. The apparatus ofclaim 78, wherein the count includes a count of occurrences of when theuser indicates that the system made a mistake.
 82. The apparatus ofclaim 78, wherein the user provides the feedback in response to thesystem requesting the feedback.
 83. The apparatus of claim 82, whereinthe feedback includes a word indicating aggravation by the user.
 84. Theapparatus of claim 82, wherein the feedback is a correction command tothe system.